Our PhD and MS level biostatisticians are highly trained in a range of statistical and analytic methods, including:
- Longitudinal data analysis
- ANOVA, regression, logistic regression
- Bayesian data analyses
- Sample size and power estimation
- Statistical genomics
- Survival analyses
- Principal component and factor analysis
- Path modeling
- Structural equation modeling
- Cluster analysis
- Complex survey data analysis
- Statistical simulations and graphics
- Profile analysis
- Gene expression data analysis
- Mixed effects models
- Generalized Estimating Equations (GEE)
- Propensity Score Matching (PSM)
- Evaluation of medical tests for classification and prediction
- Estimation of median lethal doses (LD50)/quantal dose-response curves
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Biostatisticians & Epidemiologists
Vikki G. Nolan, DSc, MPH Assistant Professor of Epidemiology, University of Memphis She received her MPH from Yale University and her doctoral degree from Boston University School of Public Health. Dr. Nolan’s primary research interests are sickle cell disease, a field she worked in for six years while at Boston University, and cancer epidemiology, which she specialized in while employed at St. Jude Children’s Research Hospital prior to joining the University of Memphis faculty. In addition to her primary research interests, she interested in and has participated in research around genetics, physical fitness, aging, community health, and infectious diseases.
Jim Wan, PhD Associate Professor of Biostatistics, Preventive Medicine Dr. Jim Y. Wan received his B.S. in Mathematics from the Chinese University of Hong Kong in 1981, & his Ph.D. in Statistics from Yale University in 1987. He collaborates with faculty across the whole campus on statistical methods for clinical and epidemiologic data and health services research. These collaborations have resulted in more than 130 peered-reviewed articles and close to 200 abstracts presented in major national and international scientific conferences. His research interest has been devoted to the analysis of failure time data. Competing risks must be taken into account in the study of how risk factors affect a specific cause of failure. In the past he studied two generalized Cox regression models in the competing risks setting. Another research interest is the use of Poisson regression and logistic regression in epidemiologic studies. This has resulted in a publication on Poisson regression.